Abstract

Urban parking systems are a significant contributor to traffic congestion and driver frustration, with studies showing that up to 30% of urban traffic is caused by drivers searching for parking. Traditional parking systems often lack real-time data and adaptability, leading to inefficiencies such as overfilled lots and underutilized spaces. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban parking by enabling real-time parking space detection, demand forecasting, and dynamic pricing. By integrating data from IoT sensors, traffic systems, and mobile applications, cities can reduce congestion, improve parking availability, and enhance the overall urban mobility experience. Experimental results demonstrate significant improvements in parking efficiency, traffic flow, and user satisfaction, offering a sustainable blueprint for smart urban parking systems.